At Hansa Cequity, we believe successful enterprises of tomorrow will be the ones who can organize and leverage customer information at speed , to optimize their marketing performance, increase accountability, improve profit and deliver growth. Hansa Cequity insights will bring to you trends and insights in this area and it's our way of sharing best practices so as to help you accelerate this culture and thinking in your organization. We call this kind of an approach Analytical Marketing and we will constantly bring in "best practices" for improving your capabilities in Analytical Marketing.

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Turbulence for Analytics-how a sexy discipline may bite the dust?


Analytics & data are words that have become very popular now. Black gold. Texas Tea are words that describe the riches that can come from data!. Information is the new oil is one of those catch statements that's now used so often (more than 1.3 million Google search results). But is Analytics over hyped now, is there more talk & less action on the ground.

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U.S. statistician and writer Nate Silver has become a legend amongst Analytics folk. His accurate prediction of the 2012 U.S. presidential election results for all 50 states elevated him to the status of celebrity geek. Television host Jon Stewart of The Daily Show called him “Lord and god of the algorithm.”Indeed, Silver’s abilities to identify the right data sources, ask the right questions and apply the right math have turned Silver into gold.

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But few companies can hire Nate Silver to run Analytics for them & so while Analytics is sexy, it has been hard to implement. Data scientists have been called the sexiest job of the 21st century. And yet it is hard to find a superman or woman who is the “god of all things for Analytics”.

Once upon a time, the most valuable secret formula in American business was CocaCola ’ s. Today it’s Google ’ s master algorithm, ” wrote Randall Stross, author of multiple books on internet - era moguls.

If you are a company thinking about either starting out in Analytics or scaling up your analytics practises, it may be a good time to think & plan for 2015. Thanks to the hype that Analytics generates, organizations have bought into the concept, but many are still unsure how to “make it happen” for them.

Recently a client asked me an interesting question: How would you start analytics in an organization? My initial reaction was to say-“right from the top”. Of course without the CEO supporting Analytics, it is not likely to be “transformational” in nature. But as I thought more about it I realised there were a few barriers that companies faced as they tried to adopt analytics.

This also seems to depend on the industry. While Banking has greater maturity in the use of Analytics, FMCG companies may not be that advanced. Also it takes time to build analytical maturity in a company. And it takes a certain unique mix of people- a combination of left & right brained!

The question was interesting from many perspectives:

  1. What exactly is analytics and does the name describe the function?

  2. How should one go about starting doing the work that analysts are supposed to do?

  3. Where should the Analytics team report-is it part of a marketing team or somewhere else?

  4. What kind of issues should analytics try and solve?

  5. What exactly is analytics and does the name describe the function?

My experience across both Retail & Retail banking has been that it is best to start small, very small! A lot of analytics can be done on an excel sheet and does not require a PhD in statistics to do. The simpler the analysis the “lesser” is the barrier in implementing the call for action that emanates from it. So my first suggestion to anyone starting out this kind of work is to follow the well know “KISS principle”(Keep it simple stupid). The most important next step from here is to choose the business area where you want to make an impact. Companies need to choose areas where their analytics work can have maximum impact. I would go for the counter intuitive bit here and try to make your analysis work for a business unit that is not doing so well. Businesses doing very well, have a lot of competing ideas clamouring for a share of the credit. It’s in the businesses that need help, that you will find maximum support. And finally I would say that choose business themes that are close to the CFO’s heart! The CFO’s support for analytics is probably the most critical part of what you would do-this forms the building blocks on which you can scale up your efforts in the years to come!

I have often come across situations where organization seem to believe that investing in top end statistical resources and buying high end technology is enough to extract value from analytics. The truth is vastly different and I strongly believe that embedding simple ideas and focussing far more on execution is critical for an organization to succeed in analytics based strategy. Using simple ideas culled from data to help fact based decisions is absolutely key. Some companies have the existing culture to support this & for many others it has to be built grounds up.

 As companies look at Analytics to give them a competitive edge, they need to make key changes in their information technology, their structure, their processes and their culture.

Culture is absolutely key to analytics adoption.

You need to ask yourself some key questions & here is a good guide from SAS on what questions to ask to assess what cultural changes are required:

1. Is my organization open to new ideas that challenge current practice?

2. Does my organization view data as a core asset?

3. Is senior management driving the organization to become more data-driven and analytical?

4. Is my organization using analytical insights to guide strategy?

5. Are we willing to let analytics help change the way we do business?

Amidst all this hype about analytics, where does “good old analysis” feature? People seem to be excited about algorithms & sexy stuff! Where is the “good old analyst” who is plain good at analysis & may not know fancy tools like SAS or R  etc. Where is the value for intelligence & not for knowing technology? In this hype about “big data”, are we getting lost in the bigness of things! Can analytics be “reinvented” to just become more intelligent & practical?

The word  Analysis means to “let loose”(from the greek-“ana” meaning “up” & lyein meaning “loosen. So Analysis is supposed to loosen up a complex problem into simpler parts. But are analysts gearing up for this or are they raring to just learn SAS or SPSS?

What should be the Analytics agenda for 2015 given the low analytics adoption?

  • Bring Analytics & customer data into the mainstream company board discussions: “It’s flummoxing that companies have better accounting for their office furniture than their information assets,” said Douglas Laney, an analyst at technology research and consulting firm Gartner Inc. “You can’t manage what you don’t measure.” Customer data is a valuable asset. Why not treat it that way? The value is not in rewarding regular purchases but in capturing information from every purchase, which builds a proprietary customer profile. Information can be a strategic advantage for companies. How many Board members are actually asking these questions?

  • Build a Creative analytics team: Bring together a team of people who are integrators & who come from the intersecting skills sets of statistics, technology & business. Analysts should not think about their job as purely high end geeks, rather they are story tellers who help make business impact happen using data. Think creatively about staffing this team-bring a journalist, tech geek & stats jock together in one group & see the magic.

  • Create a Career ladder for Analysts: Companies often neglect to career ladder the Analytics group. They don’t see analytical talent as a distinct and valuable asset to the company as a whole. They are often seen a specialist support function. And that automatically limits their career growth possibilities. But what if companies rotated their key talent through Analytics and also embedded analysts in other line functions. Companies need to overcome this neglect of analytical talent and get the most out of this critical workforce?

  • Task Analysts with Selling –their ideas: As Analytics teams or Analytics service providers-“When presenting ideas to decision makers, realize that it is your responsibility to sell – not their responsibility to buy”

  • Simplify Analytics: Make Analytics output simpler to understand for Business decision makers & tell good stories to make that happen

  • Analytics needs “Social” support: Analytics doesn’t need you to solve a technical problem but a “social” one. It is critical to simplify analytics for Business users. The key lies in demystifying analytics & linking it to practical stuff. It is also critical to start with a decision in mind & work backwards, not with the data in mind & working forward

  • Change management: Build a core competency in  driving Change management across structure, systems & process.By 2017 the CMO Will Spend More on IT Than the CIO (Gartner). So maybe you need to create a new role within Marketing called the Chief marketing Technology officer.Data led marketing needs a lot of “change management” on the ground.

  • Adopt the  Seven key dimensions of successful change management implementation: the development of a clear vision for the future; an analytics leader who acts as a change agent; successfully communicating the need for analytics; a technology solution that supports the needs of the analytics organization; support from top management; organizational culture that is compatible with the data-driven nature of analytics and continuous improvement throughout the whole change process.

  • Create a Partnership ecosystem to create rapid Analytics adoption: The single largest reason why you should consider taking on an Analytics partner is actually “speed to market” & “speed to impact”. Especially in the early stage of proving Analytics value, it needs a quick assembly of experts & very rapid turnaround of analytical tasks. Of course apart from this, many companies will not be able to meet their needs solely with in-house talent. This means that more and more advanced analytics work will be sourced externally from companies in the growing business analytics services industry. Here companies should be careful about the kind of partner they choose. Do they just want to outsource a process or are they keen to bring in consulting experience for the change management that Analytics adoption requires. (disclosure: Cequity is one such player)

  • Allow Customer privacy to take centre stage: Don’t use analytics to spam customers, respect customer privacy. When we are wearing our consumer hats, we certainly don’t like it when we feel a company is using our personal data without our approval or permission. As with other consumer behaviours privacy attitudes can be segmented. Fair Isacc has them into three categories: zealots , pragmatists , and the indifferent . Companies should be prepared to deal with all of these different attitudes

  • Link Analytics to “the last mile“: Analytics should not be expected to deliver a “Aha moment”-instead it is a factory approach to improved decisions. What are the decisions I need to make? What data do I need to make decisions? How are my decisions connected to other people ’ s decisions? What new models are required to make better decisions? So analytics is not a planning tool as much as it is a Execution tool

In their bestselling book Freakonomics, authors Steven Levitt and Stephen Dubner wrote, “If you can learn to look at data in the right way, you can explain riddles that might otherwise have seemed impossible. Because there is nothing like the power of numbers to scrub away layers of confusion and contradiction”.

Analytics practitioners have a role to play as “change agents” if they want Analytics to still exist as a profession a decade from now. They cannot sit back as passive observers watching the low pace of analytics adoption in companies!

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Otherwise you may have a case of the fictional Cybernetic revolt or robot uprising-a scenario in which an artificial intelligence (either a single supercomputer, a computer network, or sometimes a "race" of intelligent machines) decide that analysts are a threat (either to the machines or to themselves), are inferior, or are oppressors and try to destroy or to enslave them potentially leading to machine rule!

I believe Analysts have better sense than to let this happen!

The CMO’s guide to Technology & why Marketing Tech is different?


The chief marketing officer and the chief information officer have become the corporate board room’s odd couple.

According to Wall street Journal: “As marketing budgets shift to relatively newer channels like social media marketing and mobile advertising via complex advertising technology vendors, marketing executives have in recent years been tasked more and more with understanding technology — while technology executives have been pushed to understand marketing”.

But are Marketers really investing a lot in Technology. Two years back Gartner predicted that by 2017 CMO's will spend more on IT than CIO's. Is this happening? So how big is the market for marketing software today?

IDC has an answer to that question, $20.2 billion in 2014. IDC expects that the market will have a compound annual growth rate (CAGR) of 12.4% for the next five years, resulting in a $32.4 billion market by 2018.

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IDC breaks that market down into four broad categories:

  • Interaction Systems — the majority of customer-facing marketing software advertising, digital commerce, marketing automation, web experience management, mobile apps, social media tools, etc.

  • Content Production and Management — internal authoring and publishing tools, CMS platforms, DAM platforms, etc.

  • Data and Analytics — storing data and producing insights from it, such as business intelligence, predictive analytics, financial analysis, and broader marketing analytics.

  • Management and Administration — internal communications tools, workflows, budgeting, expense tracking, MRM, project management, collaboration tools, etc.

And yet Marketers are finding it difficult to adopt technology. Success rates are not so high. Vendor hype is far higher than on the ground reality. Research shows that CMO's need to be better prepared to overcome some of the challenges & one key impact item can be a stronger Technology organisation supporting the Marketing technology implementations.


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Forrester polled 308 marketing and tech leaders, finding that 44% of marketers believe the CIO hires staff with marketing experience, an improvement compared with the 19% figure from last year’s survey. But on the other side, 58% of tech leaders think that marketers actually understand marketing technology, compared with 71% of marketers who believe so, a gap indicating how marketers’ “self-confidence is inflated,” according to Forrester.

And further research showed the CMO involvement in Technology still lags

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So what should CMO's do differently in 2015 that will prepare them for this Tech invasion of Marketing.

Action Items for CMOs
          • Meet the CIO & set a common Marketing Tech budget for 2015: Does your company have a Tech budget for Marketing. Forrester research shows that only 47% of marketers believe that the CMO and CIO at their company work together to develop a tech strategy prior to allocating a budget.
          • Marketing strategy should drive Tech purchase: think through what elements of your marketing strategy you are trying to impact: Are you trying  to scale up 1:1 marketing based on analytics ?Or is it critical to create a solid data infrastructure for all the disparate data that Marketing has access to? First crystallise the strategy.
          • Carefully consider what technology you want to buy: Remember there are 947 companies at last count selling Marketing tech to you. Don’t allow a bundled sale where someone selling an Enterprise stack just bundles in some marketing software for a very low cost. Think about the implications about a wrong choice.
          • Look at building your Marketing tech operation with one primary Marketing technology provider as a hub & a few secondary best of breed point solutions as a spoke. This will allow you to get a maximum level of baseline capability from one vendor & so reduce the complexity of managing multiple technology partners.
          • Think about what changes in the structure of your marketing team are you & your company ready to make: Are you ready to hire a Chief marketing technology officer & will he report to the CIO or the CMO? Think collaboration rather than team expansion. The IT team can be your best friend if you get the structure right. Marketers who can truly understand the intersection of marketing and technology are rare. Most marketing organizations still struggle to find qualified people to support the evaluation, purchase, implementation and use of these new marketing technologies
          • Be more Process centric: CMO’s are buying a lot of technology. The intent is that it will help make us better, smarter and more efficient marketers, but with every license comes a new login and new processes that must be implemented to encompass it in our day-to-day workflows. Technology loses its value if you don’t adapt your processes to take advantage of what the software brings to the table
          • Think ROI & partner the CFO from the start: manage expectations about how quickly magic will happen, because it won’t. Process change & skill adoption takes time. Account for it. Don’t get surprised.
          • The Growth Hacker: The growth hacker is someone or a small team of people who understands technology and probably even have some coding skills. They understand their organization’s digital landscape to discover potential opportunities or loopholes. So this unit creates a Big data plan & has the all round skills to quickly create pilots & show impact. Sometimes external partners with such strengths can become your Growth hacker partners

          Customer information value is = to 0 on company balance sheets


          Customer data is a valuable asset. Why not treat it that way?

          Analytics & data are words that have become very popular now. Black gold. Texas Tea & “Information is the new oil” are those catch statements that are now used so often (more than 1.3 million Google search results). This has led to creation of “Infonomics”, a term coined by Gartner’s Doug Laney, which describes the act of quantifying, managing and leveraging information as a formal business asset. Although generally accepted accounting principles (GAAP) as yet do not require the reporting of information assets on the balance sheet, infonomics deems that organizations acknowledge that information is more than merely a resource.

          Companies are behaving as if information has a monetary value. The question is how much?

          “It’s flummoxing that companies have better accounting for their office furniture than their information assets,” said Douglas Laney, an analyst at technology research and consulting firm Gartner Inc. “You can’t manage what you don’t measure.”

          Corporate holdings of data and other “intangible assets,” such as patents, trademarks and copyrights, could be worth more than $8 trillion, according to Leonard Nakamura, an economist at the Federal Reserve Bank of Philadelphia.

          Tobin’s q is a simple ratio first posited by Nobel-winning American economist James Tobin in the 1960s to understand the relationship between a company’s market value and the replacement value of its assets. Tobin’s q has more than doubled from 0.4 in 1945 to a predicted 1.1 in any given year currently. This means that in general markets now value companies more than the sum of their tangible assets. How can this be?  Non-reportable intangible assets of course.

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          Doug Laney has this very interesting view

          “At Gartner, our infonomics research shows how information meets the criteria of a recognizable (balance sheet) asset. Yet, because the accounting aristocracy continues to prevent organizations from recognizing it, information continues to be managed with far less discipline than financial, physical assets or recognizable intangibles. We have also shown how organizations that are more information-centric have market-to-book values that are 200-300% higher than the S&P average”

          Facebook, eBay and Google have combined assets minus combined debt of $125 billion. But the combined value of shares is $660 billion. The difference reflects the stock market’s understanding that the companies’ key assets, such as search algorithms, patents and huge  information on their users and customers, don’t show up on their balance sheets.

          So how do you value your personal information? How much does the market pay for it?

          Alexis C. Madrigal has this interesting take: “ User profiles -- slices of our digital selves -- are sold in large chunks, i .e. at least 10,000 in a batch. On the high end, they go for $0.005 per profile, according to advertising-industry sources. But maybe that's not the right way to value the data. After all, each profile of you being sold only takes advantage of some subset of your information. Facebook and Google make roughly $5 and $20 per user, respectively. Without your data in one form or another, their advertising would be mostly worthless, so perhaps your data is worth something in that range. But let's not forget the rest of the Internet advertising ecosystem either, which the Internet Advertising Bureau says supported $300 billion in economic activity last year. That's more than $1,200 per Internet user and much of the online advertising industry's success is predicated on the use of this kind of targeting data”.

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          This is leading to some interesting business models:

          1. A Washington based startup, Personal wants users to entrust their data to Personal and then give it out to advertisers in exchange for discounts. Your data paired with your purchase intent could yield massive efficiency for advertisers, some of which would be passed on to you in the form of discounts.
          2. Supermarket operator Kroger Co records what customers buy at its more than 2,600 stores and also tracks the purchasing history of its roughly 55 million loyalty-card members. It sifts this data for trends and then, through a joint venture, sells the information to the vendors who stock its shelves with goods ranging from cereals to sodas. Gartner predicts that 30 percent of businesses will monetize their information assets directly by 2016. Good data is like good ingredients. Some combinations can be extremely appetizing and even sustaining. Will companies learn to share data or will the Indian crab syndrome come in the way. Consumer-products makers like Procter & Gamble Co and Nestlé SA are willing to pay for those insights because it allows them to tailor their products and marketing to consumer preferences. Gartner estimates that Kroger rakes in $100 million a year from data sales.




          Designers & Banks: Oxymorons


          What does design have to do with finance, money and banking?

          New banks in India ,IDFC & Bandhan, better be listening. A bank acquiring a creative organization- that’s news! Adaptive Path, a design and user experience consultancy has been acquired by Capitol One. And just before that Daniel Makoski, founder of Google’s modular Project Ara phone project joined Capital One.So how come banks are attracting this serious Creative talent!

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          In the new digital world, banking & creativity may not be oxymorons!

          New banks in India have a unique opportunity to embed “digital” in the fabric of how they do business. But banks are complex with structures that don’t allow for speed. In many cases, eBusiness teams own the mobile banking strategy, but few eBusiness teams have an exclusive mandate over their firm’s mobile banking initiatives. This division of responsibility creates silos and adds significant complexity to the coordination and optimization of Digital efforts.And yet, the user experience is the key for more consumers to adopt the bank’s digital channels.



          AdaptivePath CEO Brandon Schauer puts it well, when he writes:

          "Whether we talk about greeting cards, mobile apps, or vacation get-aways, the experience is the product. From the perspective of customers, everything that goes into making up that experience—technology, materials, service support, or a supply chain—simply becomes the magic behind the experience.

          Yet the orientation and focus of our businesses is the inverse of this customer perspective. We plan around features and operational functions, leaving the customer experience as an unintentional byproduct of how the pieces and parts happen to come together for the customer".

          As the infrastructure of digital technology — the chips, network connections, computing — becomes ever cheaper, they’re becoming commodities, and the value of tech products is shifting to the design and the user experience. But the real value starts to flow when companies orchestrate the User experience with Personalisation.

          Personalization, it seems, is really about gathering exactly the data that’s needed in order to perform a particular task. Think about how Amazon asks users whether purchases were for themselves or as gifts, or how streaming services like Netflix and Pandora ask users to rate content. But personalization is a complex process involving multiple components:


          The Financial services business actually can generate significant amount of user data to help personalize its offerings. Here is an interesting example of a Lending company in California, LendUp, which is doing this effectively.

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          LendUp wants to give those looking for a speedy fix to a short-term financial need, a way to borrow money without hidden fees and high interest rates. LendUp believes that really understanding its users makes all the difference in the world. The company is trying to be a low-friction source of relatively cheap loans for under banked individuals.

          LendUp’s solution is pairing smart site design with smarter algorithms.

          LendUp asks for standard data from each applicant (including Social Security number so it can look at credit scores and other data), but also asks certain applicants to connect using Twitter and Facebook. Also LendUp offers financial literacy education in the form of online webinars. Customers are incentivized to use these facilities with the knowledge that participation improves their loan rates and potentially increases their loan size in the future. LendUp found that borrowers who completed at least one of their free education modules were 80% more likely to repay. LendUp also found that repayment rates continue to rise as borrowers complete more education modules, suggesting that borrowers can apply and build on knowledge gained through a bank sponsored education program.

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          Obviously, the data LendUp generates about how people interact (by completing those credit-building lessons, for example) and repay once they’re in the system also helps the company determine future rates.

           So quite a few pointers for us as Marketers:

          1. This is the age where Creative & data intersect. If banks have realised it, many other industries can profit from it.
          2. Thinking about the user interface for consumers, making it intuitive & easy & allowing data to be picked up through gamification are possible ways ahead.
          3. Analytics can help in continously fine tuning the User interface & improving the consumer experience.

          How prostitution,alcohol & data science impact Uber ?


          If you thought Uber was just a car service company, it's a tech company that happens to be a car service too. No wonder it has neuroscientists amongst more traditional hires in the company! The data team at Uber uses data science for fundamental problems such as ETA algorithms (“Your driver will be here in 5 minutes”), pricing algorithms, fare estimators, and heat maps to show passengers the current position of their driver.

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          Uber‘s $1.2 billion financing tells a story- the imputed value for Uber (pre-money, i.e., prior to the influx of $1.2 billion) was $17 billion, a mind-boggling sum for a business that generates a couple of hundred million in revenues.

          Both Lyft (another car sharing company) & Uber have attracted massive financing. Now that each team has a quarter-billion dollars in its pocket, the World championships can begin.

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          Uber is trying to use the "movement pattern" data that it gets to more sharply understand its users. Here are 3 examples in the words of the Uber data scientists that I found fascinating:

          1. Understanding Destination choices: Where has this person gone in the past? Do they frequent a certain bar? Where do other Uber users go? What businesses are popular generally? These are the basic questions the algorithm asks. On top of that, it smartly considers factors like time of day (people don't typically go to night clubs at 11 a.m.), distance (people aren't likely to get dropped off too far from their actual destination) and even the Zip code of each destination (Sketchy neighborhood? They probably didn't want to walk far, so the destination is likely near the drop-off point).
          2. Static or dynamic drivers:In another blog post , Uber data scientist Bradley Voytek explains how Uber’s “science team” simulated a city and learned that taxi drivers can just stay parked between trips and make twice as much as those who drive around in search of passengers. Uber discovered that “drivers who are constantly, randomly moving around a simulated city travel 10-20 times the distance compared to drivers who remain stationary or gravitate back toward a demand density between trips,” Voytek writes.
          3. Prostitution, Alcohol & Uber: To examine this Uber data scientists’ looked at the correlation between the number of each type of crime and the number of trips they've done in each neighbourhood. All types of crime except murder, vehicle theft, and arson were positively correlated with number of trips. After correcting for multiple comparisons, four crimes remained significantly correlated: Prostitution,Alcohol,Theft,Burglary. In other words: The parts of San Francisco that have the most prostitution, alcohol, theft, and burglary also have the most Uber rides! Party hard but be safe, Uberites!

          So once you start to see the company as a data, tech & analytics company, the possibilities really start to become huge:

          1. Uber is now going after a huge new opportunity by changing the way business users can book and expense rides on its platform. The company is launching a new offering, called “Uber for Business”, which is designed to make it easier for users to bill trips directly to their company while working. Uber is providing participating companies with a centralized dashboard which they can use to keep track of rides that have been expensed. The new product is basically an acknowledgment that many consumers have been using Uber for both personal and business use cases, but their employers didn’t have a good way to manage those expenses.
          2. Uber has also moved into the fast-food delivery industry with its new service "UberFRESH", which it claims will deliver meals from local restaurants in less than 10 minutes. Uber isn’t taking on a fleet of new driving staff for the service. Instead it’s going to use its taxi drivers to relay the food between restaurant and customer. There will also be no extra delivery cost but drivers won’t leave their car to hand over the food, customers will have to collect it from the street.

          How relevant is all this to a company in any traditional business?  Here are some thoughts:

          1. It’s never too late to start embedding data based thinking in your business. Data based companies like Google, AirBnB, Uber etc will get to your industry at some point.
          2. Think about cross functional IT & business teams that are programmed like a guerrilla unit to solve specific problems.Get creative people into these roles.
          3. Introspect on what kind of people you are attracting for your analytics team-look for non traditional hires from the science field!
          4. Ask yourself whether you are keeping all your data. Storage is less expensive now. At Uber, they have got every GPS point for every trip ever taken at Uber, going back to the Trip #1
          5. One of the delights of using Uber is getting your receipt by email once your ride is done-for many businesses this could be a simple way of creating a customer experience & continuing to build a customer database.

            As city-wide urban infrastructures such as buses, taxis, public utilities and roads become digital, the datasets obtained can be used for tracking movement patterns through space and time.

            The identification, analysis and comparison of such patterns will provide greater insights on human movement and contribute to a better urban management and would be useful information for urban transport services provider. Imagine if Uber & a bunch of other companies started to share such data with other companies. There could be huge power in “community analytics”. If Coke & Uber were to cooperate by mashing up their data together, interesting opportunities could develop from such partnerships. Where people travel & when they consume can have interesting parallels.


            Data driven Lingerie=Better Products


            How can Lingerie & data have any correlation? I am sure you are asking that questions. But bear with me & don’t forget to watch the video that I have provided a link to.

            But before that, allow me to digress a bit. Almost 78% of consumers think it is hard to trust companies when it comes to use of their personal data (Orange, The Future of Digital Trust, 2014). And yet Personal data has become a currency today. All of us are leaving our data behind in a digital exhaust that has begun to worry us as consumers.

            So, the World Economic Forum is calling personal data a ‘new asset class’: “a valuable resource for the 21st century that will touch all aspects of society”. But companies will need to understand how they can gather customer information without compromising the customer’s trust!

            A recent PEW report had this to say:

            “While enthusiasts see great potential for using Big Data, privacy advocates are worried as more and more data is collected about people - both as they knowingly disclose things in such things as their postings through social media and as they unknowingly share digital details about themselves as they march through life. Not only do the advocates worry about profiling, they also worry that those who crunch Big Data with algorithms might draw the wrong conclusions about who someone is, how she might behave in the future, and how to apply the correlations that will emerge in the data analysis.”

            But some companies are finding a way where consumers share information because they get "value" in return.

            True & co is this interesting company that combines data & design to create an opportunity for consumers to share data with the company thereby improving the appropriateness of the product to the customer. True & co claims to be the first company to fit women into their favourite bra with a fit quiz – no fitting rooms, no measuring tape, no photos. The data they collect allows them to match the customer to over 6000 body types on their database.

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            Research suggests that women loathe the bra shopping experience and the massive $14B intimate apparel industry is dominated by one primarily brick-and-mortar player. So True & co uses big data to make shopping online for lingerie easier & better. They collect over Half a million data points from users to help customise the experience. Since the company launched in 2012, True & Co has collected some 7 million data points  They used this data to launch products designed using this data. Body type, implicit explicit preferences etc all mashed together to create a personalised recommendation engine.

            Do have a look at this video telling their story:


            So consumers are happy to share personal information as long as they see a “value add” for themselves. And organisations with trust-based information sharing relationships with customers will have significant competitive advantage over those with traditional data gathering relationships.


            Sexy Analytics, hard execution!


            I was speaking at IIM Lucknow last night & had a wonderful session with the MBA students about the changing nature of Marketing & how Big data & Analytics are going to play a big part in it.

            Prof Ashwani Kumar had a very interesting take on how Analytics is going to be eventually embedded across every function & not exist as just a separate specialisation.

            Prof Ashwani is doing some interesting work on Analytics at IIM Lucknow & you can have a look at his work & profile here:

            I spoke about how the changing nature of consumer behaviour is creating peta bytes of data for Marketers to analyse.And though Analytics is sexy today, companies still stuggle to adopt it & gain maximum mileage from it. So Analytics is popular but hard to execute! I also spoke about the need to bring creativity into analytics through both better "story telling" & more innovative approaches to data.


            And yet  in the words of a Gartner Analyst Doug Laney “companies have better sense of the value of office furniture than their information assets".

            I also spoke about how the stock market seems to value “Information based companies” far more than any others.But most companies don't report Customer data: imagine if we had Customer flow & Customer value data along with the regular balance sheet & cash flow statements!!

            Here is an interesting chart from Gartner which highlights the improved return on Information assets:

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            Here is a copy of my presentation:

            docs/IIM LucknowAug82014.pdf

            Doug Laney has this very interesting view

            “At Gartner, our infonomics research shows how information meets the criteria of a recognizable (balance sheet) asset. Yet, because the accounting aristocracy continues to prevent organizations from recognizing it, information continues to be managed with far less discipline than financial, physical assets or recognizable intangibles. We have also shown how organizations that are more information-centric have market-to-book values that are 200-300% higher than the S&P average”

            Marketing is also changing a lot because of the access to huge amounts of Social media based customer data.

            Not just marketing, Big data is hugely changing our world & life in fundamental ways. To see the enormity of this change, have a look at the video below...



            Precision journalism: Analytics needs to learn from journalists


            I have seen innumerable situations where bright analysts are unable to “tell stories from their data”. They have a lot of learning to do from an unrelated field-Journalism!
            Ben Fry has described it very well. Analytics or Data scientists need skills from these varied fields.

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            1. Computer Science - acquire and parse data
            2. Mathematics, Statistics, & Data Mining - filter and mine
            3. Graphic Design - represent and refine
            4. Infovis and Human-Computer Interaction (HCI) - interaction

            I believe that Analytics teams have a lot to learn from the new breed of Data journalists. They have all the above skills & also work with super deadlines!

            At Cequity, our model is unique because it tries to integrate very contrasting dimensions into one entity where the sum is larger than the parts! Having a designer’s sense with data may contrast with a statistician’s dry look at numbers!

            We seek “intersection” skills-intersection of Creative, technology, data & business! Not easy to do with highly talented people & we are attempting it!

            The interesting thing is that journalism is getting far savvier with data! I see visual data based story telling in the New York Times that is absolutely mind boggling. Even here in India, I see some lovely data visualization in the Mint!

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            But are analysts getting creative with their story telling? Visualization of data is getting democratized & it is not very difficult for analysts to be creative about this. Today we as consumers are getting far savvier about technology in our personal lives & that will impact our expectations at the work place. I am sure that savvy consumers will make data presentation so much more fun even within “enterprises”.

            I also wrote on this theme earlier here

            In the Media world,new business models are emerging in which data is a raw material for profit, impact, and insight, co-created with an audience that was formerly a passive consumer!

            In 2014, data journalism is mainstream and the market for data journalists is booming.New media outlets like and are competing for eyeballs with from the Mirror, from the Atlantic Media Group &The Economist’s DataBlog.
            The New York Times hired biologist and machine models expert Chris Wiggins, an associate professor of applied mathematics at Columbia University, as its chief data scientist.
            “At The New York Times, we produce a lot of content every day, but we also have a lot of data about the way people engage with that content,” Wiggins says. “[The Times] wanted to build out a data science function not only to curate and make available those data, but to learn from those data. In particular, the thing that the New York Times is interested in learning is: what makes for a good long-term relationship with a reader?”
            “On every desk in the newsroom, reporters are starting to understand that if you don’t know how to understand and manipulate data, someone who can will be faster than you, “said Scott Klein, a managing editor at ProPublica.He continued: Can you imagine a sports reporter who doesn’t know what an on-base percentage is? Or doesn’t know how to calculate it himself? You can now ask a version of that question for almost every beat. There are more and more reporters who want to have their own data and to analyze it themselves. Take, for example, my colleague, Charlie Ornstein. In addition to being a Pulitzer Prize-winner, he’s one of the most sophisticated data reporters anywhere. He pores over new and insanely complex data sets himself. He has hit the edge of Access’ abilities and is switching to SQL Server. His being able to work and find stories inside data independently is hugely important for the work he does.
            Read about this here:

            Maybe it is time for the Analytics profession to wake up & bring some variety into their hiring-a journalist amongst their midst, maybe!


            Test or get Fired! Harrah’s casino’s amazing philosophy


            Analytics needs a evangelist! Without such a person, you just don’t get the impact that Analytics actually is capable of providing! Mostly this evangelist needs to be right at the top, the CEO!

            Of course, some CMOs have led their organizations into embracing the practice, including John Costello, former exec VP-CMO of Home Depot; John Elkins, head of global brand and marketing at Visa International; and Cathy Lyons, CMO-exec VP at Hewlett-Packard.

            One organization which has become a huge case study in the application of a “fact” based approach to business is Harrah’s Enetrtainment!

            In 1998, as Harrah’s was about to embark on wave of expansion, their CEO Philip Satre asked Gary Loveman to take a break from Harvard to become chief operating officer of Harrah’s Entertainment. The important thing was the he was not brought in as a CMO but as the COO-he had the line authority to make changes that would impact the business!!

            “In terms of income, it was actually a pay cut,” Loveman says, since he had to forego the consulting that supplemented his income as a professor.

            He went on to develop the gaming industry’s most successful loyalty and analytics program—Total Rewards—which boasts more than 40 million members.

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            In an interesting article, Karl Taro Greenfeld  says this about Gary Loveman, who has since then also become the CEO:  the chief executive officer of Harrah’s Entertainment Inc., the largest gaming corporation in the world, sees his customers as a set of probabilities wrapped in human flesh.

            Since taking over as CEO in 2003, Loveman, 50, has relied on the numbers to build Harrah’s from a regional operator of 15 casinos to one with 39 in the U.S. and 13 more overseas.

            His first big move as COO was to start a loyalty program called Total Rewards, which became such a success -- growing to over 40 million members by 2010, the largest database of probabilities in the industry -- that by the time Satre stepped down in 2003, Loveman had become the logical choice to succeed him.

            Loveman earned a Ph.D. in economics at MIT and went on to become CEO, president, and chairman of Caesars Entertainment, owner of Harrah's casinos and other resorts worldwide.

            Loveman says there are three ways to get fired from the hotel and casino company: theft, sexual harassment, and running an experiment without a control group.

            But this seems like common sense, run experiments , see what works & scale up! And yet very few companies do it.

             Dan Ariely, a behavioral economics professor at Duke University and the author of Predictably Irrational, outlined some of the resistance to experimentation that he's come up against.

            “I’ve often tried to help companies do experiments, and usually I fail spectacularly,” Ariely writes. For a company struggling with getting a good bonus system in place, he suggested experiments or even just a survey. Management, he says, “didn’t want to add to the trouble by messing with people’s bonuses merely for the sake of learning. But the employees are already unhappy, I thought, and the experiments would have provided evidence for how to make them less so in the years to come.”

            But Gary Loveman managed to stay incredibly committed to Testing. These tests run from the use of coupons to offers of free meals or hotel stays, all designed to get customers to spend more money during their playtime.

            This is what he said when asked about the Testing culture: “We need to overcome hunch and intuition with empirical evidence. . . . We can start with a hunch or strong belief, but we act on it through experiment. We want evidence. We’ve gone from the introduction of experimentation as a technique to a culture of experimentation as a business discipline. We hire people predisposed to do this by temperament and by background. Organizationally, we’re committed—and I’m committed—to making sure we have the discipline to have the decisions we make informed by this evidence”.

            And yet we mustn’t forget that Harrah’s is not an easy business to run. Currently they have,$23 billion in long-term debt & have gone through some aggressive financial re structuring.

            And lastly we must also ask ourselves, is this kind of Analytics good for society! Keeping gamblers coming back may hurt them & cause a lot of turmoil in many lives! Doesn't analytics have a social responsibility!

            4 year view or a 20 year view


            I saw this wonderful video of Vinod Khosla interviewing Larry Page & Sergey Brin.

            4 year view or a 20 year view!!

            It raises some very interesting questions. What do companies need to do to grow? How should companies look at the Short term vs long term? Taking a 4 year view vs a 20 year view are two fundamentally different philosophies. It is difficult to solve a “big problem” in 4 years & easy to do in 20 years. Google, of course likes to take on “big problems”.

            So is Google a search company or will it be a larger Health company in the future. Or will it be an Artificial intelligence company?

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            Do have a look at this (long) but interesting video.


            So Short term vs Long term? How many traditional companies would invest in something like Google Brain- a machine learning initiative to help make computing more efficient and capable by mimicking the distributed processes of the human brain. And yet Artificial intelligence is more than 60 years old as an application area. One reason why some experts believe AI is beginning to achieve its long-imagined potential is the explosion of data on the web.

            So the question we need to ask is whether we have a 4 year view of Analytics or a 20 year view?

            Maybe this may lead to the following questions to ponder over:

            1. Does your company do analytics or does it compete with analytics?
            2. Does "deep personalisation" have a role to play in your company & industry?
            3. Do analytics team participate in deeper strategic & longer term decisions in the company?
            4. Do Analytics folk with their deep specialist background have the skills to participate in such initiatives?
            5. Will unsupervised techniques like AI begin to threaten the Analytics profession (as we know it now); will it reduce the need for data scientists?


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